Instructions to use SmallDoge/Doge-60M-checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SmallDoge/Doge-60M-checkpoint with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SmallDoge/Doge-60M-checkpoint", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-60M-checkpoint", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("SmallDoge/Doge-60M-checkpoint", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SmallDoge/Doge-60M-checkpoint with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SmallDoge/Doge-60M-checkpoint"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SmallDoge/Doge-60M-checkpoint",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SmallDoge/Doge-60M-checkpoint

SGLang

How to use SmallDoge/Doge-60M-checkpoint with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SmallDoge/Doge-60M-checkpoint" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SmallDoge/Doge-60M-checkpoint",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SmallDoge/Doge-60M-checkpoint" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SmallDoge/Doge-60M-checkpoint",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SmallDoge/Doge-60M-checkpoint with Docker Model Runner:
```
docker model run hf.co/SmallDoge/Doge-60M-checkpoint
```

JingzeShi commited on Feb 12, 2025

Commit

63bf44a

verified ·

1 Parent(s): 98d8631

Update README.md

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@@ -18,12 +18,12 @@ Here are the initial learning rates required to continue training at each checkp
 - **[Doge-20M](https://huggingface.co/SmallDoge/Doge-20M-checkpoint)**: 8e-3
 - **[Doge-60M](https://huggingface.co/SmallDoge/Doge-60M-checkpoint)**: 6e-3
-- **[Doge-160M]((https://huggingface.co/SmallDoge/Doge-160M-checkpoint))**: 4e-3
-- **Doge-320M**: 2e-3
 | Model | Learning Rate | Schedule | Warmup Steps | Stable Steps |
 |-------|---------------|----------|--------------|--------------|
-| Doge-20M | 8e-3 | wsd_scheduler | 800 | 6400 |
-| Doge-60M | 6e-3 | wsd_scheduler | 1600 | 12800 |
-| Doge-160M | 4e-3 | wsd_scheduler | 2400 | 19200 |
-| Doge-320M | 2e-3 | wsd_scheduler | 3200 | 25600 |

 - **[Doge-20M](https://huggingface.co/SmallDoge/Doge-20M-checkpoint)**: 8e-3
 - **[Doge-60M](https://huggingface.co/SmallDoge/Doge-60M-checkpoint)**: 6e-3
+- **[Doge-160M](https://huggingface.co/SmallDoge/Doge-160M-checkpoint)**: 4e-3
+- **[Doge-320M](https://huggingface.co/SmallDoge/Doge-320M-checkpoint)**: 2e-3
 | Model | Learning Rate | Schedule | Warmup Steps | Stable Steps |
 |-------|---------------|----------|--------------|--------------|
+| [Doge-20M](https://huggingface.co/SmallDoge/Doge-20M-checkpoint) | 8e-3 | wsd_scheduler | 800 | 6400 |
+| [Doge-60M](https://huggingface.co/SmallDoge/Doge-60M-checkpoint) | 6e-3 | wsd_scheduler | 1600 | 12800 |
+| [Doge-160M](https://huggingface.co/SmallDoge/Doge-160M-checkpoint) | 4e-3 | wsd_scheduler | 2400 | 19200 |
+| [Doge-320M](https://huggingface.co/SmallDoge/Doge-320M-checkpoint) | 2e-3 | wsd_scheduler | 3200 | 25600 |